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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >A hypothetical neural network model for generation of human precision grip
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A hypothetical neural network model for generation of human precision grip

机译：一种用于生成人精密握把的假设神经网络模型

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摘要

Humans can stably hold and skillfully manipulate an object by coordinated control of a complex, redundant musculoskeletal system. However, how the human central nervous system actually accomplishes precision grip tasks by coordinated control of fingertip forces remains unclear. In the present study, we aimed to construct a hypothetical neural network model that can spontaneously generate humanlike precision grip. The nervous system was modeled as a recurrent neural network model prescribing kinematic and kinetic constraints that must be satisfied in precision grip tasks in the form of energy functions. The recurrent neural network autonomously behaves so as to decrease the energy functions; therefore, given the estimated mass and center-of-mass location of the target object, the nervous system model can spontaneously generate muscle activation signals that achieve stable precision grips due to dynamic relaxation of the energy functions embedded in the nervous system. Fingertip forces are modulated by sensory information about slip between the object and fingertips. A two-dimensional musculoskeletal model of the human hand with a thumb and an index finger was constructed. Forward dynamic simulation of the precision grip was performed using the proposed neural network model. Our results demonstrated that the proposed neural network model could stably pinch and successfully hold up the object in various conditions, including changes in friction, object shape, object mass, and center-of-mass location. The proposed hypothetical neuro-computational model may possibly explain some aspects of the control strategy humans use for precision grip. (C) 2018 Elsevier Ltd. All rights reserved.
机译：人类可以通过对复杂的冗余肌肉骨骼系统的协调控制来稳定地握住并巧妙地操纵物体。然而，人类中枢神经系统如何通过指尖力的协调控制仍然不清楚，如何完成精确的握把任务。在本研究中，我们旨在构建一个假设的神经网络模型，可以自发地产生人类的精密夹具。神经系统被建模为经常性神经网络模型，该模型规定了运动和动力学的动态约束，必须以能量功能的形式精确地握持任务。经常性神经网络自主行为以减少能量功能;因此，鉴于目标对象的估计质量和质量中心位置，神经系统模型可以自发地产生肌肉激活信号，由于动态松弛嵌入神经系统中的能量功能而导致稳定的精确夹持。指尖力由关于物体和指尖之间的滑动的感官信息调制。构建了用拇指和食指的人手的二维肌肉骨骼模型。使用所提出的神经网络模型进行精度抓握的前向动态模拟。我们的结果表明，所提出的神经网络模型可以稳定地夹紧并成功地将物体置于各种条件下，包括摩擦，物体形状，物体质量和质量中心位置的变化。所提出的假设的神经计算模型可能解释了对控制策略人类用于精密抓握的一些方面。 （c）2018年elestvier有限公司保留所有权利。

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